Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
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DOI：
10.1145/3308558.3313607
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Weizhi Ma;Min Zhang;Yue Cao;Woojeong Jin;Chenyang Wang;Yiqun Liu;Shaoping Ma;Xiang Ren
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文献类型：
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作者：
Weizhi Ma;Min Zhang;Yue Cao;Woojeong Jin;Chenyang Wang;Yiqun Liu;Shaoping Ma;Xiang Ren
可解释性和有效性是构建推荐系统的两个关键方面。以往的工作主要集中在结合辅助信息来获得更好的推荐性能。然而，这些方法都有一些缺点：(1)基于神经网络的嵌入方法预测难于解释和调试；(2)基于符号、基于图的方法(例如基于元路径的模型)需要人工和领域知识来定义模式和规则，并且忽略了项关联类型(例如可替代和互补)。在本文中，我们提出了一种新的联合学习框架，将从知识图中归纳可解释的规则与构建规则制导的神经推荐模型相结合。该框架鼓励两个模块相辅相成地生成有效和可解释的推荐：1)从以项目为中心的知识图中挖掘归纳规则，总结出用于推理不同项目关联的常见多跳关系模式，并为模型预测提供人类可读的解释；2)推荐模块可以通过归纳规则进行扩充，从而具有更好的泛化能力来处理冷启动问题。大量的实验1表明，我们提出的方法在基于真实数据集的项目推荐方面取得了显著的改进。我们的模型在通过将项目名称链接到相关实体而生成的“噪声”项目知识图上表现出了健壮的性能。
Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate induction of explainable rules from knowledge graph with construction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments1 show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over “noisy” item knowledge graphs, generated by linking item names to related entities.